We propose to use the additional tasks of edge detection (E), semantic segmentation (S), semantic contours (C), and distance transform (D), cf. Fig. REF .
Edge detection [1]} aims to extract object boundaries.
Distance transform [2]}, in our case, is a distance function to the objects' edges.
Semantic contour [3]} produces a pixel-wise level dense classification on objects' contour.
Initially, we tried to use a continuous distance transform (i.e., without quantification).
However, we discard it because of the longer training time to convergence, and the results were comparable with the quantized distance transform.
We intuit that this behavior is due to the higher degrees of freedom when fitting a regressor.
In Appendix , we detail how to quantize the distance transform.
